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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Papale2025.IT-ridgecv")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	ReAlnet01_cornet_fov16	.663
2	ReAlnet01_cornet_fov12	.649
3	resnext101_32x48d_wsl	.632
4	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.618
5	resnet50-moclr8deg	.612
6	resnext101_32x16d_wsl	.612
7	dinov2_vitl14_lc	.612
8	regnety_032	.604
9	resnext101_32x32d_wsl	.596
10	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.594
11	convnext_xxlarge_clip	.592
12	resnet50-SIN_IN_IN	.592
13	resnet50_barlowtwins_linhead	.592
14	seresnext50_32x4d	.591
15	antialiased-r50	.590
16	focalnet_tiny_lrf_in1k	.590
17	resnet_50_v1_spiking	.587
18	resnet_50_v1	.587
19	resnet50-sup	.587
20	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.586
21	vit_large_patch14_clip_224:openai_ft_in1k	.584
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.583
23	resnet50_simsiam_linhead	.583
24	resnet50_simclr_linhead_01norm	.582
25	resnet50_byol_linhead	.581
26	resnet50-vitoimagevidnet8	.581
27	resnet50-VITO-8deg-cc	.581
28	Res2Net50_26w_4s	.581
29	ijepa_vith14_linhead	.580
30	resnet50_vicreg_linhead	.579
31	resnet50-SIN_IN	.579
32	resnet50_ewconplus_wzc	.576
33	densenet-121	.575
34	convnext_base:clip_laiona_augreg_ft_in1k_384	.574
35	voneresnet-50-non_stochastic	.572
36	dinov2_vitl14_reg_lc_featread	.571
37	dinov2_vitl14_reg_lc	.571
38	convnext_tiny_sup	.571
39	resnet152_ecoset_full	.568
40	resnet50_robust_l2_eps1	.567
41	AdvProp_efficientnet-b7	.565
42	vit_base_patch16_clip_224:openai_ft_in1k	.565
43	deit_large_imagenet_full_seed-0	.564
44	voneresnet-50	.564
45	resnet50_tutorial	.563
46	ReAlnet06_cornet	.563
47	vit_relpos_base_patch16_clsgap_224:sw_in1k	.562
48	ReAlnet03_cornet	.562
49	ReAlnet05_cornet	.561
50	AdvProp_efficientnet-b6	.560
51	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.560
52	ReAlnet02_cornet	.560
53	ReAlnet08_cornet	.560
54	ReAlnet10_cornet	.559
55	ReAlnet09_cornet	.559
56	VOneCORnet-S	.559
57	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.559
58	ReAlnet07_cornet	.558
59	ReAlnet01_cornet	.557
60	ReAlnet04_cornet	.557
61	resnet101_ecoset_full	.556
62	efficientnet_b0	.556
63	convnext_tiny:in12k_ft_in1k	.554
64	AdvProp_efficientnet-b4	.550
65	pnasnet_large_halfPrecision	.548
66	resnet_SIN_IN_FT_IN	.547
67	resnet152_imagenet_full	.546
68	AT_efficientnet-b2	.545
69	AdvProp_efficientnet-b2	.545
70	resnet101_imagenet_full	.546
71	vit_large_patch14_clip_224:laion2b_ft_in1k	.545
72	convnext_large_imagenet_full_seed-0	.543
73	shufflenet_v2_x1_0	.542
74	mehrer2020_alexnet_training_seed_01_fov12	.542
75	efficientnet_b2_imagenet_full	.540
76	resnet50_st_wzc_seed3_2_323	.538
77	efficientnet_b1_imagenet_full	.536
78	convnext_large:fb_in22k_ft_in1k	.536
79	AdvProp_efficientnet-b8	.536
80	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.535
81	deit_base_imagenet_full_seed-0	.534
82	convnext_base_imagenet_full_seed-0	.534
83	resnet50_st_wzc_seed6_2_323	.532
84	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.531
85	resnet50_st_wzc_seed7_2_323	.530
86	ViT_L_32_imagenet1k	.528
87	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.527
88	resnet50_ewconplus_wzc_seed5_2__323	.526
89	deit_small_imagenet_full_seed-0	.525
90	resnet50_imagenet_full	.524
91	resnet50_ewconplus_wzc_seed6_2_323	.524
92	effnetb1_272x240	.523
93	resnet50_ewconplus_wzc_seed2_2__323	.523
94	mobilenet_v2_1_4_224	.521
95	convnext_femto_ols:d1_in1k	.520
96	resnet50_ecoset_full	.515
97	mehrer2020_alexnet_training_seed_01_fov16	.510
98	resnext101_32x8d_wsl	.507
99	predictive_coding_pc	.506
100	resnet50-SIN	.505
101	resnet_50_v2	.504
102	custom_model_cv_18_dagger_408	.504
103	resnet-50-robust	.502
104	resnet34_imagenet_full	.502
105	cvt_cvt-13-384-in1k_4_LucyV4	.498
106	eicircuit_resnet18_bn_20260919	.497
107	cvt_cvt-21-384-in1k_4_LucyV4	.495
108	resnet18_ecoset_full	.491
109	AlexNet_SIN_fov12	.490
110	resnet18_imagenet_full	.488
111	resnet34_ecoset_full	.487
112	convnext_small_imagenet_full_seed-0	.483
113	efficientnet_b0_imagenet_full	.480
114	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.479
115	cvt_cvt-13-224-in1k_4_LucyV4	.476
116	AlexNet_SIN_fov16	.475
117	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.467
118	convnext_tiny_imagenet_full_seed-0	.463
119	mehrer2020_alexnet_training_seed_02	.459
120	convnext_xlarge:fb_in22k_ft_in1k	.459
121	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.453
122	grcnn	.448
123	resnet50_imagenet_100_seed-0	.445
124	mehrer2020_alexnet_training_seed_03	.442
125	convnext_base_fb_in1k	.442
126	mehrer2020_alexnet_training_seed_07	.441
127	cvt_cvt-21-224-in1k_4_LucyV4	.440
128	mehrer2020_alexnet_training_seed_08	.436
129	mehrer2020_alexnet_training_seed_10	.432
130	mehrer2020_alexnet_training_seed_06	.430
131	mehrer2020_alexnet_training_seed_09	.428
132	mehrer2020_alexnet_training_seed_05	.427
133	mehrer2020_alexnet_training_seed_04	.427
134	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.423
135	mehrer2020_alexnet_training_seed_01	.419
136	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.412
137	pr1_stl10_disentangled_alpha04	.401
138	convnext_small_imagenet_100_seed-0	.370
139	AlexNet_SIN	.353
140	deit_small_imagenet_100_seed-0	.330
141	ReAlnet01_cornet_fov4	.316
142	mehrer2020_alexnet_training_seed_01_fov4	.298
143	resnet18_fair	.279
144	AlexNet_SIN_fov4	.197
145	hmax	.171
146	alexnet_training_seed_02	.164
147	alexnet_training_seed_09	.159
148	alexnet_training_seed_03	.155
149	alexnet_training_seed_01	.151
150	alexnet_training_seed_10	.140
151	alexnet_training_seed_06	.135
152	alexnet_training_seed_07	.124
153	gabor_filter_mean_abs_4x4_v2	.101
154	gabor_filter_entropy_4x4	.083
155	gabor_filter_entropy_4x4_v2_forcedCosine	.078
156	gabor_filter_entropy_4x4_v2	.077
157	compact_resnet50_robust_V4	.066
158	vit_huge_clip336_laion
159	vit_large_clip336_openai
160	dinov2_vitl14_lc_res336
161	eva02_large_in22k
162	dinov2_vitg14_reg_lc
163	dinov2_vitg14_lc
164	cvt_cvt-w24-384-in22k_finetuned-in1k_4
165	vit_large_patch14_clip_336:openai_ft_in12k_in1k
166	vit_large_patch14_clip_336:laion2b_ft_in1k

Benchmark bibtex

@article{papale_extensive_2025,
	title = {An extensive dataset of spiking activity to reveal the syntax of the ventral stream},
	volume = {113},
	issn = {08966273},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S089662732400881X},
	doi = {10.1016/j.neuron.2024.12.003},
	journal = {Neuron},
	author = {Papale, Paolo and Wang, Feng and Self, Matthew W. and Roelfsema, Pieter R.},
	year = {2025},
}

Ceiling

0.98.

Note that scores are relative to this ceiling.

Data: Papale2025.IT

Metric: ridgecv